In machine condition monitoring a model is trained based on sensor data collected during the normal operation of the plant. During monitoring sensor data is used as input to the trained model and it is checked if the test data are in agreement with the normal plant operation sensor data. If the difference between actual sensor data and estimated value from the model exceeds a threshold for a sensor at a particular point in time, the monitoring system will indicate a fault for that sensor.
It is very important for the engineers to be able to view and edit the sensor properties, and especially the faults for the sensors in a fast and efficient way.
While there has been work on displaying the whole turbine machinery and showing faults at a particular window, the prior art does not teach or suggest methods that concentrated on the manner in which to display faults for a combination of sensors in time and in relation to each other on the same space. The prior art fails to teach or suggest methods for efficient viewing and editing of properties of sensors relevant for monitoring/viewing purposes and reusing them in different statistical models.
Accordingly, what is needed is a method of monitoring sensors by permitting all or substantially all of the sensors to be viewed and/or accessed by an operator in an easy and/or efficient manner.